基于遗传算法优化的拖拉机发动机剩余寿命预测模型  

Tractor Engine Remaining Life Prediction Model Based on Genetic Algorithm Optimization

作　　者：李有文[1] Li Youwen(Shanxi Institute of Mechanical&Electrical Engineering,Changzhi 046000,China)

机构地区：[1]山西机电职业技术学院,山西长治046000

出　　处：《农机化研究》2025年第6期264-268,共5页Journal of Agricultural Mechanization Research

基　　金：山西省教育科学“十四五”规划2022年度项目(GH-221002);山西省教育科学“十三五”规划2020年度教育政策研究专项(ZC-20049)。

摘　　要：拖拉机发动机剩余寿命预测对于提高工作效率、降低维修成本、延长机器寿命和保障安全运行具有重要意义。通过预测发动机剩余寿命,可以更好地进行资源规划和分配。为此,提出了一种基于遗传算法优化的拖拉机发动机剩余寿命预测模型,结合遗传算法和剩余寿命预测方法,通过优化遗传算法的参数,提高了预测模型的准确性和稳定性。同时,通过收集大量的拖拉机发动机运行数据,提取与剩余寿命相关的特征,基于遗传算法寻找最佳的特征子集建立了预测模型。最后,通过试验验证了模型在拖拉机发动机剩余寿命预测方面的有效性。结果表明:与传统的预测模型相比,基于遗传算法优化的模型具有更高的预测准确性和稳定性,RMSE为6.023,MAE仅为4.531。研究结果可以有效地应用于拖拉机发动机剩余寿命预测和维护决策中。Tractor engine remaining life prediction is important for improving efficiency,reducing maintenance costs,extending machine life and ensuring safe operation.By predicting remaining engine life,better resource planning and allocation can be made.This paper proposed a tractor engine remaining life prediction model based on genetic algorithm optimization.The model combined genetic algorithm and remaining life prediction methods,and improved the accuracy and stability of the prediction model by optimising the parameters of the genetic algorithm.In this paper,a large amount of tractor engine operation data was collected,the features associated with the remaining life were extracted and the prediction model was built based on a genetic algorithm to find the best subset of features.The results showed that the genetic algorithm optimized model had higher prediction accuracy and stability than the traditional prediction model,with RMSE of 6.023 and MAE of 4.531.The results showed that the model can be effectively applied to tractor engine remaining life prediction and maintenance decisions.

关 键 词：拖拉机发动机 寿命预测 遗传算法 神经网络 相关性 

分 类 号：S219.031[农业科学—农业机械化工程]